Data Scientist
Bloomberg · Mid-level
Next: your first full round
Do the Talent Acquisition Screen round.
One full round, as long as theirs, shows where you stand before you work on anything specific.
The rounds
Pick a round to see what it asks of you and where you stand on it.
Talent Acquisition Screen
Confirm clear communication, relevant experience, and readiness for the Data Science process.
Behavioral · 40 minSet up this round
Not tried yetNo attempt yet.
What strong sounds like
- Give a concise, ordered account of relevant experience and role interest.
- Separate your decisions and actions from the team’s broader work.
- Explain how you worked with others and handled a meaningful disagreement.
Expect to be pushed on
Ask what changed because of your actions.
Probe one collaboration difficulty and its resolution.
Shapes of problem you may get
- Concise experience and role-interest overview
- Collaboration or challenge example
- Process readiness and logistics discussion
How to prepare
Select three role-relevant examples
Choose three examples: one technical project, one disagreement, and one collaboration or challenge. Write the situation, your actions, the result, and the team dependencies for each.
Bloomberg describes this stage as a Talent Acquisition video or telephone interview. Its experienced-hire guidance asks candidates to discuss collaboration, leadership, innovation, and challenges.
Company researchHow We Hire | Bloomberg LPInterview guide | Experienced Hires | Bloomberg LP
Build a concise experience account
Prepare a two-minute account that links your recent work, direct decisions, measurable results, and interest in Bloomberg Data Science. Remove work that does not support this role.
This round needs a clear account of relevant experience and role interest. A focused account is more useful than a broad résumé summary.
Suggested approach
Rehearse ownership and disagreement
Briefly rehearse one explanation of a disagreement. State the issue, your position, what you did, how the team decided, and what changed afterward.
The round asks for individual ownership and meaningful collaboration. Naming the team decision prevents you from claiming shared outcomes as your own.
Suggested approach
Then try the round
You have three selected examples, a two-minute experience account, and clear ownership statements for each example.
Resources to focus on
- Interview guide | Experienced Hires | Bloomberg LP
Focus on “Experienced hires” and the guidance on experience, collaboration, leadership, innovation, challenges, situation, actions, and outcome.
Use its structure to organize your three examples and your explanation of why Bloomberg and this role interest you.
Your first try takes the full 40 minutes, like the real one.
What they look for
Bloomberg’s own materials describe a Talent Acquisition screen, two Data Science phone interviews, and three technical rounds during a later in-house visit. Phone topics include algorithms, problem solving, machine learning, natural language processing, and your experience. The exact order, lengths, tools, and how topics split across the in-house rounds aren’t published.
They reward
- Clear reasoning across algorithms and applied machine learning
- Evidence from owned research or project decisions
- Practical judgment under incomplete information
What to avoid
- Do not give a broad résumé summary without evidence.
- Do not claim team outcomes as your individual contribution.
- Do not jump to an algorithm before clarifying constraints.
- Do not state complexity without linking it to the chosen structure.
- Do not describe a model without its evaluation design.
How sure we are
Worth redoing if you hear something from the recruiter that contradicts this.
Confirmed · 9
- Bloomberg lists application, screening, assessment, interviews, and offer as general hiring stages. Bloomberg says the process can differ by job function and experience level.
- Bloomberg says its general screening stage uses a video or telephone interview with its in-house Talent Acquisition team.
- Bloomberg typically schedules two phone interviews for Data Science candidates before an in-house visit.
- Bloomberg says Data Science phone interviews can cover data structures, algorithms, problem solving, foundational NLP and ML, applied ML, and open-ended ML questions.
Likely · 2
- A mid-level Data Scientist should expect technical evaluation across both software engineering foundations and machine learning application, not only a review of prior projects.
- A mid-level Data Scientist should expect follow-up questions that test the depth of research or project experience as well as technical choices.
Worth asking them
Questions that fill in what the research couldn’t tell us.
How are the two Data Science phone interviews divided across algorithms, machine learning, and experience?
What format and duration does each of the three in-house technical rounds use?
Does the current process include live coding, SQL, a case study, or a presentation?
Attempts
Every round you’ve done for this job, newest first.
Talent Acquisition Screen, Phone: Algorithms and Foundations, Phone: Applied ML and Experience, In-House: Technical Foundations, In-House: Applied ML Case, and In-House: Research and Project Depth have no attempts yet.